Semisupervised Learning for Computational Linguistics

Provides a broad treatment of the theory and linguistic applications of semisupervised methods. This book discusses natural language processing methods, such as self-training and co-training. It focuses on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, SVMs, and the null-category noise model.